To address this problem, researchers have proposed federated learning (FL), a paradigm in which individual compute nodes train on their data locally, rather than sending data to a centralized server [1]}. Neural network parameters (or weights) — rather than raw data — are then shared with a central server, which returns a combined model to each of the edge nodes. In this way, all nodes benefit from their peers' training, without direct access to their peers' data. Such a method is most famously used by the Google GBoard predictive text feature on mobile phone keyboards [2]}.
